PubMed · 13260822
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A B SCHWARTZ. 1955. Pediatric references.. https://pubmed.ncbi.nlm.nih.gov/13260822/
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MOTIVATION: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.
MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
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